The real world effect of omalizumab add on therapy for patients with moderate to severe allergic asthma: The ASTERIX Observational study
Bibliographic record
Abstract
BACKGROUND: Omalizumab is a non-steroidal medication indicated for the treatment of poorly controlled moderate-to-severe allergic asthmatics. This observational study examines the "real world" effectiveness of omalizumab in this population. METHODS: This is a one year open-label observational study that compared clinical outcomes including total oral corticosteroid use, exacerbation history, measures of quality of life and inflammation in patients with moderate-to-severe allergic asthma, who were prescribed omalizumab as part of their treatment with the year prior to therapy. RESULTS: A total of 99 patients were enrolled at 25 sites in Canada. During the study period, the mean total annual OCS dose was reduced from 2301.5 mg (prednisone equivalents) in the year prior to omalizumab to 1130.0 mg (p<0.0001). There was a 71% reduction in asthma exacerbations and 56% of patients on omalizumab remained exacerbation free when compared to the year prior to study entry. Associated with this was reduced health care utilization. There were significant improvements in the Asthma Control Questionnaire (ACQ) and Asthma Quality of Life questionnaire (AQLQ) Patients with an elevated FeNO at baseline showed a better response to treatment. No new safety issues were identified during the study period. CONCLUSION: Our study demonstrates that in "real world" clinical practice, after initiating omalizumab, there is a reduction in total OCS use and exacerbation frequency in patients with moderate-to-severe allergic asthma. Patients on treatment reported improved asthma control and quality of life. FeNO may be a useful biomarker to identify patients who may benefit with omalizumab treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".